Overview

Dataset statistics

Number of variables21
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory601.1 KiB
Average record size in memory307.8 B

Variable types

Text1
Categorical6
Numeric14

Alerts

Air_Pollution is highly overall correlated with Overall_Risk_ScoreHigh correlation
Cancer_Type is highly overall correlated with GenderHigh correlation
Gender is highly overall correlated with Cancer_TypeHigh correlation
Overall_Risk_Score is highly overall correlated with Air_Pollution and 1 other fieldsHigh correlation
Risk_Level is highly overall correlated with Overall_Risk_ScoreHigh correlation
BRCA_Mutation is highly imbalanced (79.3%)Imbalance
Patient_ID has unique valuesUnique
Overall_Risk_Score has unique valuesUnique
Smoking has 169 (8.5%) zerosZeros
Alcohol_Use has 204 (10.2%) zerosZeros
Obesity has 109 (5.5%) zerosZeros
Diet_Red_Meat has 153 (7.6%) zerosZeros
Diet_Salted_Processed has 184 (9.2%) zerosZeros
Fruit_Veg_Intake has 186 (9.3%) zerosZeros
Physical_Activity has 242 (12.1%) zerosZeros
Air_Pollution has 148 (7.4%) zerosZeros
Occupational_Hazards has 192 (9.6%) zerosZeros
Calcium_Intake has 355 (17.8%) zerosZeros
Physical_Activity_Level has 214 (10.7%) zerosZeros

Reproduction

Analysis started2025-11-29 15:03:00.501285
Analysis finished2025-11-29 15:03:26.556925
Duration26.06 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Patient_ID
Text

Unique 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size107.6 KiB
2025-11-29T20:33:26.853620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2000 ?
Unique (%)100.0%

Sample

1st rowLU0000
2nd rowLU0001
3rd rowLU0002
4th rowLU0003
5th rowLU0004
ValueCountFrequency (%)
lu00001
 
< 0.1%
lu00151
 
< 0.1%
lu00021
 
< 0.1%
lu00031
 
< 0.1%
lu00041
 
< 0.1%
lu00051
 
< 0.1%
lu00061
 
< 0.1%
lu00071
 
< 0.1%
lu00081
 
< 0.1%
lu00091
 
< 0.1%
Other values (1990)1990
99.5%
2025-11-29T20:33:27.483487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02900
24.2%
3900
 
7.5%
1900
 
7.5%
2900
 
7.5%
R800
 
6.7%
6400
 
3.3%
T400
 
3.3%
S400
 
3.3%
O400
 
3.3%
C400
 
3.3%
Other values (9)3600
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02900
24.2%
3900
 
7.5%
1900
 
7.5%
2900
 
7.5%
R800
 
6.7%
6400
 
3.3%
T400
 
3.3%
S400
 
3.3%
O400
 
3.3%
C400
 
3.3%
Other values (9)3600
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02900
24.2%
3900
 
7.5%
1900
 
7.5%
2900
 
7.5%
R800
 
6.7%
6400
 
3.3%
T400
 
3.3%
S400
 
3.3%
O400
 
3.3%
C400
 
3.3%
Other values (9)3600
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02900
24.2%
3900
 
7.5%
1900
 
7.5%
2900
 
7.5%
R800
 
6.7%
6400
 
3.3%
T400
 
3.3%
S400
 
3.3%
O400
 
3.3%
C400
 
3.3%
Other values (9)3600
30.0%

Cancer_Type
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size106.1 KiB
Lung
527 
Breast
460 
Colon
418 
Prostate
305 
Skin
290 

Length

Max length8
Median length6
Mean length5.279
Min length4

Characters and Unicode

Total characters10558
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBreast
2nd rowProstate
3rd rowSkin
4th rowColon
5th rowLung

Common Values

ValueCountFrequency (%)
Lung527
26.4%
Breast460
23.0%
Colon418
20.9%
Prostate305
15.2%
Skin290
14.5%

Length

2025-11-29T20:33:27.657180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:33:27.802397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
lung527
26.4%
breast460
23.0%
colon418
20.9%
prostate305
15.2%
skin290
14.5%

Most occurring characters

ValueCountFrequency (%)
n1235
11.7%
o1141
10.8%
t1070
10.1%
s765
 
7.2%
r765
 
7.2%
e765
 
7.2%
a765
 
7.2%
u527
 
5.0%
L527
 
5.0%
g527
 
5.0%
Other values (7)2471
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1235
11.7%
o1141
10.8%
t1070
10.1%
s765
 
7.2%
r765
 
7.2%
e765
 
7.2%
a765
 
7.2%
u527
 
5.0%
L527
 
5.0%
g527
 
5.0%
Other values (7)2471
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1235
11.7%
o1141
10.8%
t1070
10.1%
s765
 
7.2%
r765
 
7.2%
e765
 
7.2%
a765
 
7.2%
u527
 
5.0%
L527
 
5.0%
g527
 
5.0%
Other values (7)2471
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1235
11.7%
o1141
10.8%
t1070
10.1%
s765
 
7.2%
r765
 
7.2%
e765
 
7.2%
a765
 
7.2%
u527
 
5.0%
L527
 
5.0%
g527
 
5.0%
Other values (7)2471
23.4%

Age
Real number (ℝ)

Distinct61
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.248
Minimum25
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:27.994546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45
Q156
median64
Q370
95-th percentile80
Maximum90
Range65
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.462946
Coefficient of variation (CV)0.1654273
Kurtosis-0.014791169
Mean63.248
Median Absolute Deviation (MAD)7
Skewness-0.18140206
Sum126496
Variance109.47323
MonotonicityNot monotonic
2025-11-29T20:33:28.173804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6485
 
4.2%
6585
 
4.2%
6881
 
4.0%
6081
 
4.0%
6777
 
3.9%
6175
 
3.8%
6273
 
3.6%
6373
 
3.6%
6672
 
3.6%
6971
 
3.5%
Other values (51)1227
61.4%
ValueCountFrequency (%)
252
 
0.1%
291
 
0.1%
311
 
0.1%
322
 
0.1%
343
 
0.1%
351
 
0.1%
361
 
0.1%
3710
0.5%
385
0.2%
395
0.2%
ValueCountFrequency (%)
906
 
0.3%
895
 
0.2%
883
 
0.1%
877
 
0.4%
867
 
0.4%
859
0.4%
845
 
0.2%
8311
0.5%
8218
0.9%
8120
1.0%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size97.8 KiB
0
1022 
1
978 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01022
51.1%
1978
48.9%

Length

2025-11-29T20:33:28.385918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:33:28.527555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01022
51.1%
1978
48.9%

Most occurring characters

ValueCountFrequency (%)
01022
51.1%
1978
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01022
51.1%
1978
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01022
51.1%
1978
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01022
51.1%
1978
48.9%

Smoking
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.157
Minimum0
Maximum10
Zeros169
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:28.645110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3253391
Coefficient of variation (CV)0.64482045
Kurtosis-1.2551944
Mean5.157
Median Absolute Deviation (MAD)3
Skewness0.055220323
Sum10314
Variance11.05788
MonotonicityNot monotonic
2025-11-29T20:33:28.781264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10343
17.2%
6267
13.4%
1204
10.2%
3175
8.8%
2174
8.7%
0169
8.5%
5166
8.3%
4166
8.3%
9126
 
6.3%
8107
 
5.3%
ValueCountFrequency (%)
0169
8.5%
1204
10.2%
2174
8.7%
3175
8.8%
4166
8.3%
5166
8.3%
6267
13.4%
7103
 
5.1%
8107
5.3%
9126
6.3%
ValueCountFrequency (%)
10343
17.2%
9126
 
6.3%
8107
 
5.3%
7103
 
5.1%
6267
13.4%
5166
8.3%
4166
8.3%
3175
8.8%
2174
8.7%
1204
10.2%

Alcohol_Use
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.035
Minimum0
Maximum10
Zeros204
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:28.920354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2609956
Coefficient of variation (CV)0.64766545
Kurtosis-1.3211465
Mean5.035
Median Absolute Deviation (MAD)3
Skewness-0.05730029
Sum10070
Variance10.634092
MonotonicityNot monotonic
2025-11-29T20:33:29.108467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7220
11.0%
2210
10.5%
0204
10.2%
9197
9.8%
8195
9.8%
1186
9.3%
10183
9.2%
6183
9.2%
3151
7.5%
4145
7.2%
ValueCountFrequency (%)
0204
10.2%
1186
9.3%
2210
10.5%
3151
7.5%
4145
7.2%
5126
6.3%
6183
9.2%
7220
11.0%
8195
9.8%
9197
9.8%
ValueCountFrequency (%)
10183
9.2%
9197
9.8%
8195
9.8%
7220
11.0%
6183
9.2%
5126
6.3%
4145
7.2%
3151
7.5%
2210
10.5%
1186
9.3%

Obesity
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9675
Minimum0
Maximum10
Zeros109
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:29.243989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median6
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0613934
Coefficient of variation (CV)0.51301105
Kurtosis-0.96720309
Mean5.9675
Median Absolute Deviation (MAD)2
Skewness-0.32497539
Sum11935
Variance9.3721298
MonotonicityNot monotonic
2025-11-29T20:33:29.406180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10348
17.4%
6217
10.8%
5210
10.5%
8208
10.4%
4208
10.4%
7190
9.5%
9181
9.0%
2121
 
6.0%
0109
 
5.5%
1109
 
5.5%
ValueCountFrequency (%)
0109
5.5%
1109
5.5%
2121
6.0%
399
5.0%
4208
10.4%
5210
10.5%
6217
10.8%
7190
9.5%
8208
10.4%
9181
9.0%
ValueCountFrequency (%)
10348
17.4%
9181
9.0%
8208
10.4%
7190
9.5%
6217
10.8%
5210
10.5%
4208
10.4%
399
 
5.0%
2121
 
6.0%
1109
 
5.5%

Family_History
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size97.8 KiB
0
1611 
1
389 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01611
80.5%
1389
 
19.4%

Length

2025-11-29T20:33:29.570440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:33:29.707062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01611
80.5%
1389
 
19.4%

Most occurring characters

ValueCountFrequency (%)
01611
80.5%
1389
 
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01611
80.5%
1389
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01611
80.5%
1389
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01611
80.5%
1389
 
19.4%

Diet_Red_Meat
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1895
Minimum0
Maximum10
Zeros153
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:29.821698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1544516
Coefficient of variation (CV)0.60785271
Kurtosis-1.1571786
Mean5.1895
Median Absolute Deviation (MAD)3
Skewness-0.0079247227
Sum10379
Variance9.950565
MonotonicityNot monotonic
2025-11-29T20:33:29.945326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10261
13.1%
5206
10.3%
7201
10.1%
6189
9.4%
4188
9.4%
3180
9.0%
2170
8.5%
1169
8.5%
0153
7.6%
8150
7.5%
ValueCountFrequency (%)
0153
7.6%
1169
8.5%
2170
8.5%
3180
9.0%
4188
9.4%
5206
10.3%
6189
9.4%
7201
10.1%
8150
7.5%
9133
6.7%
ValueCountFrequency (%)
10261
13.1%
9133
6.7%
8150
7.5%
7201
10.1%
6189
9.4%
5206
10.3%
4188
9.4%
3180
9.0%
2170
8.5%
1169
8.5%

Diet_Salted_Processed
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5635
Minimum0
Maximum10
Zeros184
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:30.105470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0883226
Coefficient of variation (CV)0.6767443
Kurtosis-1.0428935
Mean4.5635
Median Absolute Deviation (MAD)2
Skewness0.30094034
Sum9127
Variance9.5377366
MonotonicityNot monotonic
2025-11-29T20:33:30.235988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4260
13.0%
3253
12.7%
2229
11.5%
1187
9.3%
0184
9.2%
10182
9.1%
6167
8.3%
5151
7.5%
9136
6.8%
7126
6.3%
ValueCountFrequency (%)
0184
9.2%
1187
9.3%
2229
11.5%
3253
12.7%
4260
13.0%
5151
7.5%
6167
8.3%
7126
6.3%
8125
6.2%
9136
6.8%
ValueCountFrequency (%)
10182
9.1%
9136
6.8%
8125
6.2%
7126
6.3%
6167
8.3%
5151
7.5%
4260
13.0%
3253
12.7%
2229
11.5%
1187
9.3%

Fruit_Veg_Intake
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9275
Minimum0
Maximum10
Zeros186
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:30.396074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0453047
Coefficient of variation (CV)0.61802226
Kurtosis-1.0837447
Mean4.9275
Median Absolute Deviation (MAD)2
Skewness0.018508932
Sum9855
Variance9.2738807
MonotonicityNot monotonic
2025-11-29T20:33:30.522596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4214
10.7%
6214
10.7%
3214
10.7%
5210
10.5%
8195
9.8%
0186
9.3%
10162
8.1%
1159
8.0%
7154
7.7%
2146
7.3%
ValueCountFrequency (%)
0186
9.3%
1159
8.0%
2146
7.3%
3214
10.7%
4214
10.7%
5210
10.5%
6214
10.7%
7154
7.7%
8195
9.8%
9146
7.3%
ValueCountFrequency (%)
10162
8.1%
9146
7.3%
8195
9.8%
7154
7.7%
6214
10.7%
5210
10.5%
4214
10.7%
3214
10.7%
2146
7.3%
1159
8.0%

Physical_Activity
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.015
Minimum0
Maximum10
Zeros242
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:30.659158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9784578
Coefficient of variation (CV)0.74183257
Kurtosis-0.84281018
Mean4.015
Median Absolute Deviation (MAD)2
Skewness0.45586305
Sum8030
Variance8.8712106
MonotonicityNot monotonic
2025-11-29T20:33:30.810795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1261
13.1%
3254
12.7%
4246
12.3%
0242
12.1%
2225
11.2%
5173
8.6%
6152
7.6%
8117
5.9%
10112
5.6%
7111
5.5%
ValueCountFrequency (%)
0242
12.1%
1261
13.1%
2225
11.2%
3254
12.7%
4246
12.3%
5173
8.6%
6152
7.6%
7111
5.5%
8117
5.9%
9107
5.3%
ValueCountFrequency (%)
10112
5.6%
9107
5.3%
8117
5.9%
7111
5.5%
6152
7.6%
5173
8.6%
4246
12.3%
3254
12.7%
2225
11.2%
1261
13.1%

Air_Pollution
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.323
Minimum0
Maximum10
Zeros148
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:30.947325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2074624
Coefficient of variation (CV)0.60256667
Kurtosis-1.2113638
Mean5.323
Median Absolute Deviation (MAD)3
Skewness0.0032898067
Sum10646
Variance10.287815
MonotonicityNot monotonic
2025-11-29T20:33:31.175884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10310
15.5%
4243
12.2%
3211
10.5%
8172
8.6%
5169
8.5%
6161
8.1%
2160
8.0%
0148
7.4%
7144
7.2%
9143
7.1%
ValueCountFrequency (%)
0148
7.4%
1139
7.0%
2160
8.0%
3211
10.5%
4243
12.2%
5169
8.5%
6161
8.1%
7144
7.2%
8172
8.6%
9143
7.1%
ValueCountFrequency (%)
10310
15.5%
9143
7.1%
8172
8.6%
7144
7.2%
6161
8.1%
5169
8.5%
4243
12.2%
3211
10.5%
2160
8.0%
1139
7.0%

Occupational_Hazards
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.979
Minimum0
Maximum10
Zeros192
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:31.358521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2128991
Coefficient of variation (CV)0.64529003
Kurtosis-1.1775541
Mean4.979
Median Absolute Deviation (MAD)3
Skewness0.074888453
Sum9958
Variance10.32272
MonotonicityNot monotonic
2025-11-29T20:33:31.540078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5243
12.2%
10238
11.9%
4212
10.6%
0192
9.6%
1182
9.1%
3173
8.6%
9168
8.4%
2164
8.2%
6152
7.6%
7146
7.3%
ValueCountFrequency (%)
0192
9.6%
1182
9.1%
2164
8.2%
3173
8.6%
4212
10.6%
5243
12.2%
6152
7.6%
7146
7.3%
8130
6.5%
9168
8.4%
ValueCountFrequency (%)
10238
11.9%
9168
8.4%
8130
6.5%
7146
7.3%
6152
7.6%
5243
12.2%
4212
10.6%
3173
8.6%
2164
8.2%
1182
9.1%

BRCA_Mutation
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size97.8 KiB
0
1935 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01935
96.8%
165
 
3.2%

Length

2025-11-29T20:33:31.716227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:33:31.852781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01935
96.8%
165
 
3.2%

Most occurring characters

ValueCountFrequency (%)
01935
96.8%
165
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01935
96.8%
165
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01935
96.8%
165
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01935
96.8%
165
 
3.2%

H_Pylori_Infection
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size97.8 KiB
0
1607 
1
393 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01607
80.3%
1393
 
19.7%

Length

2025-11-29T20:33:31.989527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:33:32.119066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01607
80.3%
1393
 
19.7%

Most occurring characters

ValueCountFrequency (%)
01607
80.3%
1393
 
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01607
80.3%
1393
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01607
80.3%
1393
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01607
80.3%
1393
 
19.7%

Calcium_Intake
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9405
Minimum0
Maximum10
Zeros355
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:32.271805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0488699
Coefficient of variation (CV)0.77372665
Kurtosis-0.95611768
Mean3.9405
Median Absolute Deviation (MAD)3
Skewness0.34952778
Sum7881
Variance9.2956076
MonotonicityNot monotonic
2025-11-29T20:33:32.398957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0355
17.8%
1210
10.5%
5205
10.2%
4202
10.1%
2201
10.1%
3192
9.6%
6181
9.0%
7166
8.3%
10110
 
5.5%
890
 
4.5%
ValueCountFrequency (%)
0355
17.8%
1210
10.5%
2201
10.1%
3192
9.6%
4202
10.1%
5205
10.2%
6181
9.0%
7166
8.3%
890
 
4.5%
988
 
4.4%
ValueCountFrequency (%)
10110
5.5%
988
4.4%
890
4.5%
7166
8.3%
6181
9.0%
5205
10.2%
4202
10.1%
3192
9.6%
2201
10.1%
1210
10.5%

Overall_Risk_Score
Real number (ℝ)

High correlation  Unique 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45444884
Minimum0.029284507
Maximum0.85215847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:32.586105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.029284507
5-th percentile0.25396347
Q10.36698156
median0.45539924
Q30.53978165
95-th percentile0.66163792
Maximum0.85215847
Range0.82287396
Interquartile range (IQR)0.17280009

Descriptive statistics

Standard deviation0.12307394
Coefficient of variation (CV)0.27082023
Kurtosis-0.29088733
Mean0.45444884
Median Absolute Deviation (MAD)0.086175833
Skewness0.016484704
Sum908.89768
Variance0.015147194
MonotonicityNot monotonic
2025-11-29T20:33:33.191900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3986960981
 
0.1%
0.3666889261
 
0.1%
0.3268656481
 
0.1%
0.2391700431
 
0.1%
0.4415670821
 
0.1%
0.344843961
 
0.1%
0.5288869771
 
0.1%
0.4768887631
 
0.1%
0.4972510351
 
0.1%
0.3597922621
 
0.1%
Other values (1990)1990
99.5%
ValueCountFrequency (%)
0.0292845071
0.1%
0.0869296231
0.1%
0.1049065811
0.1%
0.1105326491
0.1%
0.1125484261
0.1%
0.1261869561
0.1%
0.1291694851
0.1%
0.1430595751
0.1%
0.1432269551
0.1%
0.1479603481
0.1%
ValueCountFrequency (%)
0.8521584681
0.1%
0.8140661871
0.1%
0.8135083811
0.1%
0.7707903011
0.1%
0.7695617031
0.1%
0.7658898071
0.1%
0.7658270161
0.1%
0.7640080791
0.1%
0.7548686391
0.1%
0.753024071
0.1%

BMI
Real number (ℝ)

Distinct208
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.18335
Minimum15
Maximum41.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:33.378164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19.7
Q123.5
median26.2
Q328.7
95-th percentile32.705
Maximum41.4
Range26.4
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation3.9474585
Coefficient of variation (CV)0.15076217
Kurtosis0.012211472
Mean26.18335
Median Absolute Deviation (MAD)2.6
Skewness0.047668228
Sum52366.7
Variance15.582429
MonotonicityNot monotonic
2025-11-29T20:33:33.582313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.934
 
1.7%
26.326
 
1.3%
2525
 
1.2%
26.825
 
1.2%
24.124
 
1.2%
26.724
 
1.2%
26.123
 
1.1%
25.123
 
1.1%
23.423
 
1.1%
26.522
 
1.1%
Other values (198)1751
87.5%
ValueCountFrequency (%)
156
0.3%
15.22
 
0.1%
15.41
 
0.1%
15.51
 
0.1%
15.61
 
0.1%
15.81
 
0.1%
15.91
 
0.1%
161
 
0.1%
16.11
 
0.1%
16.32
 
0.1%
ValueCountFrequency (%)
41.41
0.1%
38.81
0.1%
38.61
0.1%
38.31
0.1%
36.91
0.1%
36.61
0.1%
36.51
0.1%
36.42
0.1%
36.32
0.1%
36.22
0.1%

Physical_Activity_Level
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9385
Minimum0
Maximum10
Zeros214
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-11-29T20:33:33.738838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1660274
Coefficient of variation (CV)0.6410909
Kurtosis-1.2055324
Mean4.9385
Median Absolute Deviation (MAD)3
Skewness-0.010345111
Sum9877
Variance10.02373
MonotonicityNot monotonic
2025-11-29T20:33:33.870843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0214
10.7%
5204
10.2%
7195
9.8%
9191
9.6%
2188
9.4%
6177
8.8%
3172
8.6%
4171
8.6%
8166
8.3%
10165
8.2%
ValueCountFrequency (%)
0214
10.7%
1157
7.8%
2188
9.4%
3172
8.6%
4171
8.6%
5204
10.2%
6177
8.8%
7195
9.8%
8166
8.3%
9191
9.6%
ValueCountFrequency (%)
10165
8.2%
9191
9.6%
8166
8.3%
7195
9.8%
6177
8.8%
5204
10.2%
4171
8.6%
3172
8.6%
2188
9.4%
1157
7.8%

Risk_Level
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size106.4 KiB
Medium
1574 
Low
324 
High
 
102

Length

Max length6
Median length6
Mean length5.412
Min length3

Characters and Unicode

Total characters10824
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium1574
78.7%
Low324
 
16.2%
High102
 
5.1%

Length

2025-11-29T20:33:34.013473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:33:34.144071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
medium1574
78.7%
low324
 
16.2%
high102
 
5.1%

Most occurring characters

ValueCountFrequency (%)
i1676
15.5%
M1574
14.5%
e1574
14.5%
d1574
14.5%
u1574
14.5%
m1574
14.5%
L324
 
3.0%
o324
 
3.0%
w324
 
3.0%
H102
 
0.9%
Other values (2)204
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1676
15.5%
M1574
14.5%
e1574
14.5%
d1574
14.5%
u1574
14.5%
m1574
14.5%
L324
 
3.0%
o324
 
3.0%
w324
 
3.0%
H102
 
0.9%
Other values (2)204
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1676
15.5%
M1574
14.5%
e1574
14.5%
d1574
14.5%
u1574
14.5%
m1574
14.5%
L324
 
3.0%
o324
 
3.0%
w324
 
3.0%
H102
 
0.9%
Other values (2)204
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1676
15.5%
M1574
14.5%
e1574
14.5%
d1574
14.5%
u1574
14.5%
m1574
14.5%
L324
 
3.0%
o324
 
3.0%
w324
 
3.0%
H102
 
0.9%
Other values (2)204
 
1.9%

Interactions

2025-11-29T20:33:24.086409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:01.761039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.551341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:05.520715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.671024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.444792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.155863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.885331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.476344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.025177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:17.909603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.436275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.987782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.526693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.202043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:01.944080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.662858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:05.647228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.797545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.581300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.288420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.994854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.585974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.139818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.020138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.542865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.104321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.632212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.342685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.066596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.783369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.061315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.923331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.700811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.395012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.107442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.710622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.239413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.122763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.663922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.207873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.755976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.490749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.227636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.888887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.175827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.041236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.809808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.515567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.223042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.816224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.342040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.230401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.769519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.318361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.857110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.607304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.342150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:04.029923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.292345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.200991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.939328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.646716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.345648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.919738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.457075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.333917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.886051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.431960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.961430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.714974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.459660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:04.139434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.425388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.345252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.044357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.762242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.448316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.028257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.566685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.439519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.013592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.535572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.092992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.854492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.575174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:04.285962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.538897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.501021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.160875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.868480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.563941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.135451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.674350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.560632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.117217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.640188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.196559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:24.964717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.688688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:04.454006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.669412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.607699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.293901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.977048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.689462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.240005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.793865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.667158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.225848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.768357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.301185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:25.075380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.806211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:04.658067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.797930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.728937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.423934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.096675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.801065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.361057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:16.901500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.771679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.330474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.873915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.407817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:25.241911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:02.947235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:04.820106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:06.935971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.835111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.553199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.246843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:13.911619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.487646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:17.015033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.894736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.433105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:21.996156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.513413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:25.356118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.066750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:05.036134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.066485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:08.956292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.691827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.391547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.026256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.595756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:17.118819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:18.999268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.538670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.104760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.628045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:25.488690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.214784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:05.155646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.186998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.069482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.808455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.511085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.141786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.697385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:17.222423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.104787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.661379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.207803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.730679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:25.605311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.320302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:05.294161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.406455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.216236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:10.916121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.640643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.245302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.818980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:17.670574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.224318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.765042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.308432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.862294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:25.716319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:03.426826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:05.411681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:07.532503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:09.325748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:11.039720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:12.768697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:14.372836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:15.922540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:17.782088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:19.331836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:20.867225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:22.411061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-29T20:33:23.961888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-29T20:33:34.292742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAir_PollutionAlcohol_UseBMIBRCA_MutationCalcium_IntakeCancer_TypeDiet_Red_MeatDiet_Salted_ProcessedFamily_HistoryFruit_Veg_IntakeGenderH_Pylori_InfectionObesityOccupational_HazardsOverall_Risk_ScorePhysical_ActivityPhysical_Activity_LevelRisk_LevelSmoking
Age1.000-0.024-0.031-0.0110.0000.0530.219-0.053-0.0690.0400.0140.2910.036-0.0060.027-0.0450.057-0.0460.0310.030
Air_Pollution-0.0241.0000.0700.0280.0000.0540.268-0.0780.0320.000-0.0450.0000.000-0.0710.0850.5000.0770.0040.2800.461
Alcohol_Use-0.0310.0701.000-0.0130.034-0.0630.000-0.023-0.0270.0000.0400.0240.0590.0070.0010.3860.0370.0180.2230.114
BMI-0.0110.028-0.0131.0000.0820.0220.0000.036-0.0100.000-0.0120.0000.000-0.0030.0020.034-0.004-0.0040.047-0.006
BRCA_Mutation0.0000.0000.0340.0821.0000.0560.1480.0000.0000.0000.0190.0720.0000.0390.0000.0270.0180.0000.0000.020
Calcium_Intake0.0530.054-0.0630.0220.0561.0000.1740.0990.0520.014-0.0200.3030.033-0.1110.0750.055-0.006-0.0060.0410.073
Cancer_Type0.2190.2680.0000.0000.1480.1741.0000.3010.1550.0000.1340.6120.0470.2210.2060.1410.0000.0660.1630.361
Diet_Red_Meat-0.053-0.078-0.0230.0360.0000.0990.3011.0000.1840.000-0.1880.0580.000-0.051-0.0100.264-0.0040.0340.160-0.145
Diet_Salted_Processed-0.0690.032-0.027-0.0100.0000.0520.1550.1841.0000.000-0.2230.0000.144-0.0370.0580.352-0.023-0.0040.204-0.059
Family_History0.0400.0000.0000.0000.0000.0140.0000.0000.0001.0000.0000.0000.0000.0160.0290.0400.0300.0520.0530.000
Fruit_Veg_Intake0.014-0.0450.040-0.0120.019-0.0200.134-0.188-0.2230.0001.0000.0000.1570.010-0.050-0.1470.017-0.0100.1010.040
Gender0.2910.0000.0240.0000.0720.3030.6120.0580.0000.0000.0001.0000.0000.2160.0480.0580.0790.0820.0000.133
H_Pylori_Infection0.0360.0000.0590.0000.0000.0330.0470.0000.1440.0000.1570.0001.0000.0420.0470.0080.0710.0000.0140.097
Obesity-0.006-0.0710.007-0.0030.039-0.1110.221-0.051-0.0370.0160.0100.2160.0421.0000.0010.2170.0110.0200.125-0.090
Occupational_Hazards0.0270.0850.0010.0020.0000.0750.206-0.0100.0580.029-0.0500.0480.0470.0011.0000.360-0.0020.0430.177-0.010
Overall_Risk_Score-0.0450.5000.3860.0340.0270.0550.1410.2640.3520.040-0.1470.0580.0080.2170.3601.0000.0480.0460.8180.434
Physical_Activity0.0570.0770.037-0.0040.018-0.0060.000-0.004-0.0230.0300.0170.0790.0710.011-0.0020.0481.0000.0230.0700.100
Physical_Activity_Level-0.0460.0040.018-0.0040.000-0.0060.0660.034-0.0040.052-0.0100.0820.0000.0200.0430.0460.0231.0000.0060.020
Risk_Level0.0310.2800.2230.0470.0000.0410.1630.1600.2040.0530.1010.0000.0140.1250.1770.8180.0700.0061.0000.243
Smoking0.0300.4610.114-0.0060.0200.0730.361-0.145-0.0590.0000.0400.1330.097-0.090-0.0100.4340.1000.0200.2431.000

Missing values

2025-11-29T20:33:25.949486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-29T20:33:26.358390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Patient_IDCancer_TypeAgeGenderSmokingAlcohol_UseObesityFamily_HistoryDiet_Red_MeatDiet_Salted_ProcessedFruit_Veg_IntakePhysical_ActivityAir_PollutionOccupational_HazardsBRCA_MutationH_Pylori_InfectionCalcium_IntakeOverall_Risk_ScoreBMIPhysical_Activity_LevelRisk_Level
0LU0000Breast68072805374631000.39869628.05Medium
1LU0001Prostate74189800371330050.42429925.49Medium
2LU0002Skin5517107033418100060.60508228.62Medium
3LU0003Colon61062206246480080.31844932.17Low
4LU0004Lung67110740631091090050.52435825.12Medium
5LU0005Lung7711083060621070000.49866825.11Medium
6LU0006Lung59010100094011090050.66235432.32High
7LU0007Prostate74186213328870010.47936729.19Medium
8LU0008Colon7119030104610830050.49762024.15Medium
9LU0009Skin55171200425990050.40483728.21Medium
Patient_IDCancer_TypeAgeGenderSmokingAlcohol_UseObesityFamily_HistoryDiet_Red_MeatDiet_Salted_ProcessedFruit_Veg_IntakePhysical_ActivityAir_PollutionOccupational_HazardsBRCA_MutationH_Pylori_InfectionCalcium_IntakeOverall_Risk_ScoreBMIPhysical_Activity_LevelRisk_Level
1990ST0390Skin52110506107371000100.49471729.22Medium
1991ST0391Lung5601001087631080070.59133229.06Medium
1992ST0392Colon4611290101009450160.48389030.81Medium
1993ST0393Lung630103900554910050.42299928.810Medium
1994ST0394Skin671516030405100040.34353227.45Medium
1995ST0395Colon601464010644531040.43753930.33Medium
1996ST0396Prostate841578010012130020.45112825.94Medium
1997ST0397Lung650721004223600100.29576022.53Low
1998ST0398Lung641102100210754200100.42220125.33Medium
1999ST0399Breast640341000510390000.51813723.03Medium